Aims and Scope
This journal seeks to provide the latest research and the best practices in the field of data mining. This journal provides theoretical and pragmatic viewpoints on data mining and also provide guidance to the professionals who will use this journal to inform their practices. The journal incorporate various advanced data mining techniques and algorithms of various integrated data mining systems and applications. Implementation of advanced data mining techniques will be very helpful for scientists, engineers, and physicians as well as researchers, and infrastructures that support them. In addition to these, as researchers revealed data mining is the process of automatic discovery of patterns, transformations, associations and anomalies in massive databases, and is a highly interdisciplinary field representing the confluence of multiple disciplines, such as database systems, data warehousing, machine learning, statistics, algorithms, data visualization, and high-performance computing.
Coverage: Data Mining for Business Intelligence, Emerging technologies in data mining, Data mining tools, Bio-Informatics, Data Mining and Warehousing, Information extraction methodologies, Genetic algorithms and categorization techniques used in data mining, Data and information integration, Microarray design and analysis, Privacy-preserving data mining, Statistical methods used in data mining, Knowledge Discovery in Databases (KDD), Information Retrieval, Metadata use and management, Search engine query processing, Data Models and Access Structures, Semantic Web and Ontology, Advanced User Interfaces for Data Mining, Visual Languages for Data Mining, Engineering and Manufacturing, Enterprise Data Mining and Management, Geographic and Environmental Information Systems, Intrusion detection, Fraud prevention, and Surveillance.